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Clark Quinn’s Learnings about Learning

Experience, the API

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Last week I was on a panel about the API previously known as Tin Can at #DevLearn, and some thoughts crystallized.  Touted as the successor to SCORM, it’s ridiculously simple: Subject Verb Object: e.g. “I did this”, such as ‘John Doe read Engaging Learning’ but also ‘Jane Doe took this picture’.  And this has interesting implications.

First, the API itself is very simple, and while it can be useful on it’s own, it’ll be really useful when there’re tools around it.  It’s just a foundation upon which things can be done.  There’ll need to be places to record these actions, and ones to pull together sequences of recommendations for learning paths, and more.  You’ll want to build portfolios of what you’ve done (not just what content you’ve touched).

But it’s about more than learning.  These can cross accessing performance support resources, actions in social media systems, and more. This person touched that resource. That person edited this file.  This other person commented.

One big interesting opportunity is to be able to start mining these.  We can start looking at evidence of what folks did and finding good and bad outcomes.  It’s a consistent basis for big data and analytics.  It’s also a basis to start customizing: if the people who touched this resource were better able to solve problem X, other people with that problem maybe should also touch it. If they’ve already tried X and Y, we can next recommend Z.  Personalization/customization.

An audience member asked what they should take back to their org, and who needed to know what.  My short recommendations:

Developers need to start thinking about instrumenting everything.  Everything people touch should report out on their activity.  And then start aggregating this data.  Mobile, systems, any technology touch. People can self report, but it’s better to the extent that it’s automated.

Managers need to recognize that they’re going to have very interesting opportunities to start tracking and mining information as a basis to start understanding what’s happening.  Coupled with rich other models, like of content (hence the need for a content strategy), tasks, learners, we can start doing more things by rules.

And designers need to realize, and then take advantage of, a richer suite of options for learning experiences.  Have folks take a photo of an example of X.  You can ask them to discuss Y.  Have them collaborate to develop a Z.  You could even send your learners out to do a flash mob ;).

Learning is not about content, it’s about experience, and now we have ways to talk about it and track it. It’s just a foundation, just a standard, just plumbing, just a start, but valuable as all that.

Learning There

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A respected colleague recently suggested Andy Clark’s Being There as a read to characterize the new views of cognition, so I checked it out.  The book covers the new emerging views of cognition, grounded in the connectionist revolution and incorporating a wide variety of neural and robotic studies. The interesting thing to me are the implications for learning and instruction.

The book makes the case that the way we think is not only heavily tied to our contexts, but that we co-construct the world in ways that affect our thinking in profound ways.  Studies across economic behavior, animal cognition, simulation studies, and more are integrated to make the point that they way we think is very different than the models of conscious minds sitting in meat vehicles.  Instead, we’re very driven from below and outside, and our conscious thinking is rare, hard, and language based.  Moreover, the constructs we create to think affect our thinking, making it easier. We automate much not only through learning, but we externalize.  And, our representations and understanding are very much constructed ‘on the fly’ in each new situations, as opposed to existing abstract and robust.

This isn’t easy reading.  Clark is a philosopher of mind, and covers much complex research and deep neuroscience.  The emergent picture, however, is of a mind very different than the cognitivist model.  I’m grateful that while I pursued my PhD in Cog Psych, the research going on in our co-shared lab by Rumelhart and McClelland on connectionist networks sensitized me to this viewpoint, and Hutchins work on Cognition in the Wild was similarly taking place at the same time.   Despite the challenge, there are important reasons to get our minds around this way of thinking.

The notion that providing abstract knowledge will lead to any meaningful outcome has already pretty much been debunked both empirically and theoretically.  What these models seem to suggest is that what can and will work is deep scaffolded practice and guided reflection, based upon a situated cognition. For other reasons, this is the model that Collins and Brown had in Cognitive Apprenticeship, and now we’ve a more solid philosophical basis for it.  (I also think that there are rejoinders to Kirschner, Sweller, & Clark, and Anderson, Reder, & Simon; discussing how language, including writing, is social, and that iterations between abstract models and meaningful practice is guided reflection.)

This model suggests that language is our differentiator, and that much of our higher level cognition is mediated through language.  There’s a reason consciousness feels like a dialog.  Much of our processing is beneath consciousness, and things we monitor and develop through language become compiled away inaccessible to language.

The point, to me, is that the activity-based learning model I’ve proposed has both bottom up grounding in new cognitive models, theoretical framing from anchored instruction and social constructivism, as well as empirical validity from apprenticeships and work-place learning. We need to start aligning our learning design to the cognitive realities.

Wise organizations

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My ITA colleague Jay (always a spark igniter) has been thinking about well-being in organizations, and it activated my thinking on wisdom.  My interest in  wisdom  continues to ferment, slowly but surely, as a personal commitment.  My question was what would business wisdom look like, and what would be the benefits?

One preliminary issue is definitional: when I google the term, I mostly see good business practices wrapped up and trumped as business wisdom.  That’s not quite what I mean.  We’ve seen examples of people doing things that were smart in the moment, but not very smart over time (*cough* Enron *cough*).  Yes, there are some business principles that really do stand the test of time and could be considered business wisdom, but I’m thinking more about wise decisions, not wise principles.  Other folks tend to treat wisdom as ineffable or only obvious  in situ, you know it when you see it but you can’t analyze it. That doesn’t leave me much traction, so I focus on frameworks that give me some possibility for doing things differently.

So the definition I like for wisdom comes from Robert Sternberg, where he talks about making decisions that are not just smart in the short term, but in the long term.  Decisions that consider not just me and mine, but society in general. And decisions that are based on values that are articulated and examined, not implicit and potentially less then optimal.  I suggest that this sort of approach would lead to better decisions.

One of the things would be just to get people to start making decisions with this approach.  If you accept the view that for situations where we’re experts, we can trust our gut, this means more to slow down when we’re making decisions out of our comfort zone. It’s harder work, to be very conscious in our decision making process, but I hope it’s implicitly obvious that making better decisions is the best solution.

And this segues into the broader topic of the organizational culture.  I’m not immune to the view that there’s a certain personal attitude to wisdom. The wisest people I know are also the most unflappable, thoughtful and warm.  And I think that’s hard to accomplish in an organization where everything you say can and will be held against you.  You’ve got to have the appropriate culture for such an approach to flourish. Which ties to Jay’s interest in well-being, bring me full circle.

So, I think there’s an argument to be made for consider wisdom in business, as part of a longer term shift from short term returns to a sustainable differentiator. Coupled with appropriation of collaboration and cooperation, I suggest organizations can and should be working wiser and more coherently.

 

Top 10 Tools for Learning

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Among the many things my colleague Jane Hart does for our community is to compile the Top 100 Tools for learning each year.  I think it’s a very interesting exercise, showing how we ourselves learn, and the fact that it’s been going on for a number of years provides interesting insight.  Here are my tools, in no particular order:

WordPress  is how I host and write this Learnlets blog, thinking out loud.

Keynote is how I develop and communicate my thinking to audiences (whether I eventually have to port to PPT for webinars or not).

Twitter is how I track what people find interesting.

Facebook is a way to keep in touch with a tighter group of people on broader topics than just learning. I’m not always happy with it, but it works.

Skype is a regular way to communicate with people, using a chat as a backchannel for calls, or keeping open for quick catch ups with colleagues.  An open chat window with my ITA colleagues is part of our learning together.

OmniGraffle is the tool I use to diagram, one of the ways I understand and communicate things.

OmniOutliner often is the way I start thinking about presentations and papers.

Google is my search tool.

Word is still the way I write when I need to go industrial-strength, getting the nod over Pages because of it’s outlining and keyboard shortcuts.

GoodReader on the qPad is the way I read and markup documents that I’m asked to review.

That’s 10, so I guess I can’t mention how I’ve been using  Graphic Converter to edit images, or  GoToMeeting as the most frequent (tho’ by no means the only) web conferencing environment I’ve been asked to use.

I exhort you to also pass on your list to Jane, and look forward to the results.

Emergent & Semantic Learning

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The last of the thoughts still percolating in my brain from #mlearncon finally emerged when I sat down to create a diagram to capture my thinking (one way I try to understand things is to write about them, but I also frequently diagram them to help me map the emerging conceptual relationships into spatial relationships).

Semantic and Emergent rules for contentWhat I was thinking about was how to distinguish between emergent opportunities for driving learning experiences, and semantic ones.  When we built the Intellectricity© system, we had a batch of rules that guided how we were sequencing the content, based upon research on learning (rather than hardwiring paths, which is what we mostly do now).  We didn’t prescribe, we recommended, so learners could choose something else, e.g. the next best, or browse to what they wanted.  As a consequence, we also could have a machine learning component that would troll the outcomes, and improve the system over time.

And that’s the principle here, where mainstream systems are now capable of doing similar things.  What you see here are semantic rules (made up ones), explicitly making recommendations, ideally grounded in what’s empirically demonstrated in research.  In places where research doesn’t stipulate, you could also make principled recommendations based upon the best theory.  These would recommend objects to be pulled from a pool or cloud of available content.

However, as you track outcomes, e.g. success on practice, and start looking at the results by doing data analytics, you can start trolling for emergent patterns (again, made up).  Here we might find confirmation (or the converse!) of the empirical rules, as well as potentially  new patterns that we may be able to label semantically, and even perhaps some that would be new.  Which helps explain the growing interest in analytics.  And, if you’re doing this across massive populations of learners, as is possible across institutions, or with really big organizations, you’re talking the ‘big data’ phenomena that will provide the necessary quantities to start generating lots of these outcomes.

Another possibility is to specifically set up situations where you randomly trial a couple alternatives that are known research questions, and use this data opportunity to conduct your experiments. This way we can advance our learning more quickly using our own hypotheses, while we look for emergent information as well.

Until the new patterns emerge, I recommend adapting on the basis of what we know, but simultaneously you should be trolling for opportunities to answer questions that emerge as you design, and look for emergent patterns as well.  We have the capability (ok, so we had it over a decade ago, but now the capability is on tap in mainstream solutions, not just bespoke systems), so now we need the will.  This is the benefit of thinking about content as systems – models and architectures – not just as unitary files.  Are you ready?

 

Stealth mentoring

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I was looking for any previous post I’d made about stealth mentoring, so I could refer to it in a post I was writing, and I couldn’t find it. It’s a concept I refer to often (and have to give credit to my colleague Jay Cross who inspired the thought), so here’s my obligatory place holder.

When someone is thinking and learning ‘out loud’, e.g. putting their deeper reflections on line via, say, a blog (er, like this one, recursively), they’re allowing you to look at where and how their thinking is going.  When they also are leaving a trail of what they think is interesting (e.g. by pointing to things on Twitter or leaving bookmarks at a social bookmarking site), you can put together what’s interesting to them and what their resulting thoughts are, and start seeing the trajectory of their thinking and learning.

In formal learning, we can think of modeling behavior and cognitive annotation, the processes covered in Cognitive Apprenticeship as a development process. In a more informal sense, if you had a leader who shared discussions of their thinking with you, you’d consider that  mentoring.

Similarly, here, with a difference.  If they’re blogging and tweeting, or otherwise leaving tracks of their thinking, they can be mentoring you and not even know it. You’re being a stealth mentee!  So, if you can find interesting people who blog and tweet a lot, and you follow their blogs and tweets, they can be mentors to you!

I strongly recommend this path to self-development. One of the ways to accelerate your own growth, part of your personal knowledge management path, is to mentor folks who represent the type of thinking you believe is interesting and important.  By the way, don’t just consume, interact.  If they say something you don’t understand or disagree with, engage: either you’ll learn, or they will.

And, as an associated caveat, I strongly recommend that you also similarly share your thinking.  You can be not only stealth mentored, but folks who read and comment become actual real mentors for you, shaping your thinking. The feedback I’ve gotten through comments on my blog has been extremely beneficial to improving my own thinking, and I’m very grateful.

I really do think this is an important opportunity for personal self-development, and it’s a benefit of the increasing use of social media. I hope you are practicing learning out loud and leaving traces of what’s interesting you as you wander hither and yon. I think it’s something an app like Tappestry could provide as well, leveraging the Tin Can API, where you might more explicitly see a richer picture of what someone’s doing.  But I’m getting into the weeds here, so I’ll simply point out that there’s an opportunity here. You owe it to others to think and learn out loud, and then can take advantage of others who do so with a clear conscience.